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Implementation and Validation of a Digital Privacy Framework for Crowdsourced IoT Data Processing

Year: 2025

Type: article

Abstract: The proposed privacy-preserving crowdsourcing framework integrates IoT-based anomaly detection with differential privacy, homomorphic encryption, and federated learning. The framework protects data gathering, anonymization, and processing while ensuring accurate outcomes. The framework is built using Flask and Isolation Forest, which ensures the balance between utility and privacy while providing security for crowdsourced IoT applications for real-world deployment.

Source: Journal of Information Systems Engineering & Management

DOI: 10.52783/jisem.v10i21s.3453

Author: Santosh Kumar, Mohammad Faisal

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4408675637

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